Substance abuse
Serious psychiatric illness Negative beliefs/
attitudes Lack of faith in treatment effectiveness Belief that an asymptomatic disease
does not require treatment
Misinformation about medication or disease
Medication/
Provider Level Financial High drug costs High copayments
Other Negative side-effects of medication Multiple or complex medication
regimens
Lack of follow-up
Lack of patient education
Importantly, many barriers to adherence are surmountable, and researchers, practitioners, and policymakers have developed and tested various interventions designed to help patients overcome these barriers. The remainder of this chapter will review some of these medication adherence interventions, with a special focus on reinforcement-based behavioral economics interventions.
of these interventions involve family or peer support combined with comprehensive education and counseling from health care professionals.
Because these complex interventions often produce only modest improvements in adherence, some researchers have investigated a simpler approach to improving medication adherence: minimizing or eliminating financial barriers.
Increasing adherence with traditional economics
The high cost of prescription drugs and insurance copayments are major barriers to adherence. In a nationwide survey of Medicare beneficiaries conducted in 2004, the most frequently cited reason for not filling a prescription was, “thought it would cost too much” (56%), followed by,
“medicine not covered by insurance” (20%; Kennedy et al., 2008). Further, several studies have reported inverse relations between medication adherence and the cost of copayments.
Huskamp and colleagues (2003) examined prescription drug use among participants enrolled in an employer-sponsored health plan before and after the employer increased copayments for certain brand-name drugs. After the change was implemented, enrollees were significantly more likely to switch to less expensive generic or brand-name drugs than employees in a control group. Moreover, many enrollees simply stopped refilling their prescriptions rather than switching to more affordable medications. Thus, increases in copayments were associated with decreases in medication adherence.
Research has also shown decreases in copayments are associated with increases in adherence. After Blue Cross Blue Shield of North Carolina reduced copayments for brand-name drugs and eliminated copayments for generic drugs, medication adherence significantly improved for patients with diabetes, high cholesterol, hypertension, and congestive heart failure (Maciejewski et al., 2010).
This relationship between cost sharing and medication adherence is remarkably systematic. A comprehensive review of 132 articles examining associations between medication adherence and various cost sharing features of prescription drug benefits showed increased cost sharing was associated with nonadherence, and for every 10 percent increase in patient cost sharing, prescription drug spending decreased by 2 percent to 6 percent (Goldman et
al., 2007).
Taken together, data from these studies show the demand for prescription medications is price sensitive. Thus, armed with traditional economic theory, an increasing number of employers and insurers are combatting medication nonadherence with value-based insurance designs (VBIDs) that minimize or eliminate financial barriers. These cost-sharing plans require lower copayments for medications that provide greater therapeutic benefits (Chernew et al., 2007).
Thus far, research suggests VBIDs are associated with statistically significant increases in medication adherence among patients with chronic health conditions (e.g., Farley et al., 2012). Notably, however, most studies report only modest (1% to 5%) increases in adherence to targeted drugs.
Further, the long-term effects of VBIDs on morbidity, mortality, and health care costs remain unclear. Nevertheless, if this traditional economic approach to promoting medication adherence is adopted nationwide, then small improvements in adherence could have major public health repercussions.
Although medication adherence interventions based on traditional economic theory have produced promising, albeit modest, improvements in adherence, there is certainly room for additional improvement. Consequently, an increasing number of medication adherence interventions are emerging from the field of behavioral economics.
Increasing adherence with behavioral economics
Traditional economics (or rational choice) theory predicts patients will adhere to their prescriptions when the long-term benefits of doing so outweigh the short-term costs. In other words, this theory suggests patients will pay a relatively small monetary fee today for a medication that will produce relatively large health benefits in the future. However, this theoretical perspective does not take into account the behavioral economics of intertemporal choice (i.e., the influence of time on decision-making) or, more specifically, delay discounting (i.e., the tendency to discount the value of future consequences relative to present consequences).
Medication adherence is operant behavior (i.e., behavior maintained and modifiable by its consequences), and like other health-related operant behavior, delay discounting plays an important role in adherence. A brief
appraisal of the immediate and delayed consequences associated with medication adherence reveals why this behavior is an ideal target for behavioral economics intervention.
The short-term beneficial consequences of medication adherence often go unnoticed (e.g., if the health condition is asymptomatic or the drug effects are imperceptible). Consequently, patients may not recognize the benefits of adherence (e.g., improved health or longer life) for many weeks, months, or years after they start taking a drug. Due to this delay, patients are likely to discount the benefits of medication adherence, thereby, limiting the influence of these consequences on their behavior. Conversely, the costs associated with adherence, although comparatively modest (e.g., the cost of a copayment, visiting a pharmacy to pick up refills, drug side effects), are more immediate and, therefore, have greater influence on behavior. For some patients, these short-term response costs outweigh the long-term benefits associated with medication adherence, resulting in nonadherence.
Some behavioral economics interventions are designed to shift the balance between the short-term and long-term consequences of adherence by minimizing the response costs associated with the behavior. For example, CVS/caremarkTM offers an automated refill program called ReadyFill at Mail® in which CVS pharmacies will refill and mail eligible prescriptions to enrollees prior to the prescription refill date. This type of intervention could help improve medication adherence by minimizing the effort required by patients to refill their prescriptions.
To date, there are limited data on the efficacy and acceptability of ReadyFill at Mail® (Keller et al., 2011). Presumably, however, behavioral economics interventions that minimize or eliminate the response costs associated with refilling a prescription are likely to increase refills. Similarly, traditional economic interventions that minimize or eliminate the financial costs associated with refilling a prescription are also likely to increase refills.
Yet, medication adherence involves more than just refilling prescriptions once every one to three months. Patients must also take their medications—
often daily. An intervention that targets each of these responses may be necessary to increase adherence for some patients.
Reinforcement-based interventions are behavioral economics interventions that provide proximate reinforcement (e.g., small cash incentives) when patients take a dose of medication as prescribed. Numerous studies have shown reinforcement-based interventions can increase medication adherence
in patients suffering from a variety of health conditions. Several examples of these interventions are discussed below and outlined in Table 7.2.
Methadone-based reinforcement of disulfiram adherence
The rate of alcohol abuse is high in opioid-dependent patients, and alcohol use can interfere with methadone maintenance treatment (Ottomanelli, 1999).
Disulfiram administration may help reduce alcohol consumption in this population. Disulfiram inhibits acetaldehyde metabolism, causing patients to become ill (i.e., experience symptoms such as headache and nausea) when they consume alcohol. The drug is effective in reducing alcohol consumption when patients are adherent; however, its clinical utility is limited by very low rates of adherence (20% to 50%; Wright and Moore, 1990). Thus, Liebson et al. (1978) developed one of the first reinforcement-based medication adherence interventions in an effort to promote disulfiram adherence (Table 7.2).
In this study, male methadone patients who were recently or soon to be discharged from treatment due to alcohol-related problems were randomly assigned to one of two groups. In a reinforcement group, patients received methadone doses contingent on supervised ingestion of disulfiram. In a control group, patients received methadone regardless of disulfiram adherence. The percentage of patients’ drinking days were assessed with breath alcohol tests and found to be much lower in the reinforcement group (2%) relative to the control group (21%). An increased rate of employment and fewer arrests were also observed among patients in the methadone-based reinforcement group. Although disulfiram adherence was not objectively measured in this study, the authors reported that control patients admitted they were not taking their disulfiram.
Results from Liebson et al. (1978) suggested methadone-based reinforcement could be used to promote medication adherence, even to a drug with notoriously low rates of adherence. Subsequent investigations showed methadone-based reinforcement could also be used to improve medication adherence among opioid-dependent patients with other health problems (e.g., Elk et al., 1993). Of course, it may be unethical to deny methadone to patients who refuse to adhere to disulfiram or other medications. Thus, Liebson et al. only enrolled patients who were already at risk for discharge from a methadone treatment program.
TABLE 7.2 Reinforcement-based interventions to promote medication adherence
Importantly, take-home methadone doses can also be used as reinforcers in methadone clinics (e.g., Stitzer et al., 1977). Patients who are awarded this clinic privilege are given an extra dose of methadone to take home with them so they will not have to return to the clinic to pick up another dose the following day.
Employment-based reinforcement of naltrexone adherence
Naltrexone is an opioid antagonist that blocks the reinforcing effects of heroin and other opioids. Due to the drug’s mechanism of action and its lack of abuse potential, it holds considerable promise as a relapse prevention tool for opioid dependent patients. However, evidence for its clinical utility is mixed. Authors of a Cochrane review of oral naltrexone studies noted adherence was too low in most studies to draw any definitive conclusions about the efficacy of the drug (Minozzi et al., 2011). Thus, to determine the clinical utility of naltrexone, methods are needed to improve adherence.
Although some studies have shown monetary-based reinforcement of naltrexone ingestion can promote adherence (e.g., Preston et al., 1999), most interventions were of relatively short duration (e.g., 12 weeks). Given the chronic nature of opioid dependence, interventions of longer duration are
needed. However, with increasing duration of reinforcement-based interventions comes increasing cost.
Silverman and colleagues developed an innovative strategy for increasing treatment duration without increasing cost: employment-based reinforcement (Silverman et al., 2001). With employment-based reinforcement, patients are permitted access to a “therapeutic workplace” contingent on objective evidence of target behavior change. Patients who are given access to the workplace typically earn $10 per hour for data entry jobs. This treatment method has been applied to a number of health-related behaviors, including oral naltrexone adherence in unemployed injection drug users (Dunn et al., 2013; Table 7.2).
In Dunn et al. (2013), opioid-dependent patients were inducted onto oral naltrexone after detox and then randomized to one of two 26-week conditions. Patients assigned to an employment-based reinforcement group were given access to the therapeutic workplace contingent on supervised ingestion of oral naltrexone. Patients assigned to a control group received a take-home supply of the drug and were permitted access to the workplace regardless of naltrexone adherence. Urine samples were collected monthly to assess adherence in both groups. The mean percentage of naltrexone-positive urine samples was significantly higher among patients in the reinforcement group (72%) relative to the control group (21%). Moreover, all urine samples were naltrexone-positive for 43 percent of reinforcement patients compared to only 3 percent of control patients.
Although oral naltrexone requires frequent dosing (e.g., three times per week), extended-release injectable formulations of the drug have been developed that require less frequent dosing. For example, Vivotrol® requires injections once every four weeks. A study that examined employment-based reinforcement of adherence to depot injections of Vivotrol® showed the intervention significantly improved adherence relative to controls (74% vs.
26%; DeFulio et al., 2012).
Notably, neither oral nor extended release naltrexone adherence was associated with significantly fewer opioid-positive urine samples in these studies. Thus, more research is needed to determine the clinical utility of naltrexone in preventing relapse in opioid dependent patients, especially polydrug using patients. Nevertheless, these studies show that employment- based reinforcement can substantially improve medication adherence, as rates of naltrexone adherence were three times higher in the employment-based
reinforcement groups relative to the control groups.
Cash-based reinforcement of antipsychotic medication adherence
Psychiatric illness is a major barrier to medication adherence (Table 7.1). In a systematic review of adherence studies, the mean rate of medication adherence was 76 percent among patients with physical disorders compared to 58 percent among patients with psychiatric disorders (Cramer and Rosenheck, 1998).
Staring and colleagues conducted a pilot study to evaluate cash-based reinforcement of medication adherence in patients diagnosed with schizophrenia who had a recent history of nonadherence and hospitalization (Staring et al., 2010; Table 7.2). The study was conducted in the Netherlands, and patients received 10 to 20 Euro (approximately 15 to 30 USD at the time the study was conducted) contingent on each depot injection of antipsychotic medication. The magnitude of the reinforcer depended on the frequency of the dosing schedule: €10 when injections occurred every two weeks, €15 when they occurred every three weeks, and €20 when they occurred every four weeks.
Patients in this study accepted 44 percent of injections during the year prior to intervention implementation compared to 100 percent of injections when cash-based reinforcers were available during the second year. Further, mean time spent in the hospital fell from 100 days during year one to just three days during year two. Although this study was not designed to evaluate the efficacy of the intervention, it demonstrated that cash-based reinforcement is feasible for increasing medication adherence in a difficult- to-treat population, and the intervention was associated with remarkably robust improvements in outcome measures.
Several randomized controlled trials have also shown that modest monetary reinforcers can be used to promote adherence to various forms of pharmacotherapy (e.g., Seal et al., 2003; Tulsky et al., 2000). One advantage of monetary reinforcement is it relies on a well-established system of generalized conditioned reinforcement. Where some programmed consequences such as methadone and employment only function as reinforcers for individuals who are experiencing a relevant motivating operation (e.g., opioid withdrawal or unemployment), cash functions as a reinforcer for virtually everyone, regardless of an individual’s current state of motivation.
Token-based reinforcement of isoniazid adherence in children
Token economies are among the most common reinforcement-based interventions. They were first developed for use in inpatient psychiatric clinics but have subsequently been adopted to improve a variety of behaviors in a range of settings often among children (Kazdin, 1982). In a token economy, conditioned reinforcers (e.g., coins or stickers) are awarded contingent on desirable behavior change, and after a specified number of tokens are earned, they are exchanged for primary or “back-up” reinforcers (e.g., goods or privileges).
Cass and colleagues evaluated a token-based reinforcement intervention to promote medication adherence in children and adolescents diagnosed with latent tuberculosis (Cass et al., 2005; Table 7.2). Patients were 14 years of age or younger and prescribed a 6- to 9-month regimen of daily isoniazid (a first-line treatment for active and latent tuberculosis). At intake, these patients received a 1-month calendar and 30 stickers. They were instructed to place a sticker on the calendar each day they took their medication. When a completed calendar was returned at monthly clinic visits, patients could select a toy or stuffed animal from a “treasure chest.” At each visit, patients received another calendar and a new supply of stickers. Relative to a historical comparison group, patients who received token-based reinforcement were significantly more likely to be considered adherent by attending at least six of their monthly clinic appointments (92% vs. 82%).
Daily oral medication adherence was not objectively measured by Cass et al. (2005). Rather, staff relied on patient self-report. Ideally, reinforcement- based interventions should rely on objective measures of adherence such as supervised medication ingestion. In studies where more objective measures were obtained, methadone-based (Elk et al., 1993) and cash-based reinforcement have been shown to increase isoniazid adherence (Tulsky et al., 2000). Thus, token-based reinforcement should also improve adherence when tokens are awarded contingent upon supervised ingestion of isoniazid or other drugs. If so, then the token economy could be a very promising tool that health care professionals could use in inpatient treatment settings and parents could use at home with nonadherent children.
Prize-based reinforcement of antiretroviral medication adherence
Although objective assessment of adherence is an important feature of reinforcement-based interventions, direct supervision of medication ingestion may be difficult or impossible for outpatient treatment providers. Some medication regimens require multiple doses per day, but patients may be unwilling or unable to travel to a clinic to demonstrate adherence on a daily basis. Thus, to obtain reliable and valid estimates of adherence, some researchers have used medication event monitoring system (MEMS) caps.
These caps rely on electronic sensors to record the time and date each time a prescription drug bottle is opened.
Rosen et al. (2007) used MEMS caps to assess adherence to antiretroviral medication in HIV positive patients (Table 7.2). After baseline rates of adherence were assessed, patients who were less than 80 percent adherent to their medication regimens were randomized to one of two 16-week conditions. Patients assigned to a counseling group received weekly individual counseling sessions. Patients assigned to a reinforcement group received weekly counseling plus prize-based contingency management.
Prize-based contingency management is an intervention that relies on a probabilistic or intermittent schedule of reinforcement (Petry, 2000). Thus, it may be more affordable than some alternative reinforcement-based interventions, and it is becoming increasingly popular in clinical settings (e.g., Petry et al., 2014).
At counseling sessions, patients in the reinforcement group reviewed MEMS data from the previous week. For each day that all medication doses were taken within an agreed-upon 3-hour window, these patients earned one opportunity to win a prize (i.e., they could draw one slip of paper from a prize bowl), and they earned an additional five prize draws when all doses during the week were taken on time. The majority of the slips of paper in the prize bowl read, “good job” but were not associated with any tangible reinforcement. Over one-fourth of the slips could be exchanged for $1 prizes (e.g., bus tokens and fast-food gift certificates), a smaller proportion could be exchanged for $20 prizes (e.g., clothing and retail gift cards), and less than 1 percent could be exchanged for $100 prizes (e.g., small appliances and electronics).
Relative to baseline measures, mean rates of adherence increased significantly more for patients in the reinforcement group (from 61% to 76%) compared to patients in the counseling group, whose mean rates of adherence actually decreased from 59 percent to 44 percent. Moreover, patients in the
reinforcement group were significantly more likely to achieve 95 percent adherence over the course of the 16-week intervention, and they had significantly improved viral loads. Thus, prize-based reinforcement of MEMS-confirmed adherence not only improved medication adherence, it also improved health indices.
Notably, other studies have also demonstrated the feasibility and efficacy of reinforcing MEMS-confirmed medication adherence (e.g., Sorensen et al., 2007). Although this approach is promising, two important limitations must be considered. First, patients can open MEMS caps without ingesting the medication. Thus, they could receive programmed consequences without actually taking their medication. Second, when used in outpatient settings, this strategy may require substantial delays between the target behavior and reinforcer delivery. Behavioral economics research shows that such delays can compromise treatment efficacy (e.g., Lussier et al., 2006). Recently, however, researchers have been developing reinforcement-based interventions that can overcome these limitations by using mobile phone technology to remotely monitor and reinforce medication adherence in real time.
Remote voucher-based reinforcement of antihypertensive medication adherence
Petry et al. (2015) developed and tested a mobile phone-based intervention to promote medication adherence (Table 7.2). Patients with high blood pressure (≥120 mmHg systolic and 80 mmHg diastolic) were invited to participate if they took antihypertensive medication for at least three months and reported missing doses within the past month. Patients were randomized to one of two 12-week conditions. Those assigned to a standard care condition attended a 30-minute session on improving medication adherence and were instructed to visit their physician as usual. Patients assigned to a reinforcement condition received standard care and were instructed to video-record themselves ingesting each dose of antihypertensive medication with a mobile phone and send the videos to research staff. When staff received the date- and time- stamped videos confirming adherence during the appropriate 4-hour dosing window, they replied with text messages that included an electronic statement of voucher earnings (e.g., “Great job! You earned $1 for taking your medication on time today!”). Vouchers, like tokens, function as conditioned reinforcers, which can later be exchanged for retail goods or gift cards